Abstract
Mobile game-based learning (MGBL) exploits an entertaining environment for providing digital education. Such an approach involves the construction of students’ groups for gaming towards advancing their knowledge. However, building adequate groups has important pedagogical implications, since the recommendation of appropriate collaborators could further enhance the students’ cognitive abilities. Towards this direction, this paper presents a MGBL application for the tutoring of computer programming. In this application, the system recommends to each student four peers to play with as competitors using genetic algorithm. The genetic algorithm finds the most adequate peers for each student by taking into consideration students’ learning modality, previous knowledge, current knowledge and misconceptions. As such, the student can select from the list one person from the proposed ones, who share common characteristics. The two main reasons why homogeneous groups are chosen to be formed are to promote fair competition and to provide adaptive game content based on players’ characteristics for improving their learning outcomes. Our MGBL application was evaluated using students’ t-test with promising results.
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References
Chen, C.M., Kuo, C.H.: An optimized group formation scheme to promote collaborative problem-based learning. Comput. Educ. 133, 94–115 (2019)
Cahyana, U., Paristiowati, M., Savitri, D.A., Hasyrin, S.N.: Developing and application of mobile game based learning (M-GBL) for high school students performance in chemistry. Eurasia J. Math. Sci. Technol. Educ. 13(10), 7037–7047 (2017)
Chen, C.P.: Understanding mobile English-learning gaming adopters in the self-learning market: the uses and gratification expectancy model. Comput. Educ. 126, 217–230 (2018)
Melero, J., HernÁndez-Leo, D.: Design and implementation of location-based learning games: four case studies with “QuesTInSitu: the game. IEEE Trans. Emerg. Top. Comput. 5(1), 84–94 (2016)
Yoon, D.M., Kim, K.J.: Challenges and opportunities in game artificial intelligence education using angry birds. IEEE Access 3, 793–804 (2015)
Lescano, G., Costaguta, R., Amandi, A.: Genetic algorithm for automatic group formation considering student’s learning styles. In: 2016 8th Euro American Conference on Telematics and Information Systems (EATIS), pp. 1–8, April 2016
Moreno, J., Ovalle, D.A., Vicari, R.M.: A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics. Comput. Educ. 58(1), 560–569 (2012)
Tien, H.W., Lin, Y.S., Chang, Y.C., Chu, C.P.: A genetic algorithm-based multiple characteristics grouping strategy for collaborative learning. In: International Conference on Web-Based Learning, pp. 11–22, October 2013
Liu, L., Chen, L., Shi, C., Chen, H.: The study of collaborative learning grouping strategy in Intelligent Tutoring System. In: The 2010 14th International Conference on Computer Supported Cooperative Work in Design, pp. 642–646, April 2010
Chittaro, L.: Designing serious games for safety education: “Learn to Brace” versus traditional pictorials for aircraft passengers. IEEE Trans. Vis. Comput. Graph. 22(5), 1527–1539 (2015)
Hamari, J., Shernoff, D.J., Rowe, E., Coller, B., Asbell-Clarke, J., Edwards, T.: Challenging games help students learn: an empirical study on engagement, flow and immersion in game-based learning. Comput. Hum. Behav. 54, 170–179 (2016)
Hwang, G.J., Chang, S.C.: Effects of a peer competition-based mobile learning approach on students’ affective domain exhibition in social studies courses. Br. J. Educ. Technol. 47(6), 1217–1231 (2016)
Krouska, A., Troussas, C., Virvou, M.: Applying genetic algorithms for student grouping in collaborative learning: a synthetic literature review. Intell. Decis. Technol. 13(4), 395–406 (2019)
Krouska, A., Virvou, M.: An enhanced genetic algorithm for heterogeneous group formation based on multi-characteristics in social networking-based learning. IEEE Trans. Learn. Technol. (2019)
Falkenauer, E.: The grouping genetic algorithms-widening the scope of the GAs. Belg. J. Oper. Res. Stat. Comput. Sci. 33(1), 2 (1992)
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Krouska, A., Troussas, C., Sgouropoulou, C. (2020). Applying Genetic Algorithms for Recommending Adequate Competitors in Mobile Game-Based Learning Environments. In: Kumar, V., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science(), vol 12149. Springer, Cham. https://doi.org/10.1007/978-3-030-49663-0_23
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DOI: https://doi.org/10.1007/978-3-030-49663-0_23
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